The present invention relates generally to data analysis techniques, and more specifically to a process for reducing the contamination of data by extraneous readings superimposed on data values by noise sources.
Data analysis is often complicated by the problem of determining true values of measurements from those contaminated by extraneous signals normally referred to as "noise." The most common method of alleviating this problem is to average the raw data over some period of time so that the resulting mean values approximate the true readings. This approach is effective in some cases, especially when the noisy signals are symmetrically superimposed upon a data base in both magnitude and polarity. Data averaging may also be effectively employed on random noise if the data are averaged over log time periods. Extraneous random readings do not exhibit a preference in their deviations from a data base and will, over a suitably long time period, tend to cancel one another to give accurate mean values.
There are, however, circumstances where averaging may not produce acceptable results, such as in situations of severe noise that that are biased in magnitude and polarity. In fact, numerical averaging can only successfully repress noise (to the point that smoothed data will closely approximte uncontaminated base data) if the extraneous readings of the noise have symmetry in polarity and symmetry in magnitude. Such instances are rare. Whenever noise exhibits either asymmetry in polarity, asymmetry in magnitude, or asymmetry in both polarity and magnitude, then conventional numerical averaging will exhibit a similar asymmetry in attempting to approximate uncontaminated data.
In view of the foregoing discussion, it is apparent that there currently exists the need for a data-analysis process that reduces the contamination of data by noise. The present invention is intended to satisfy that need.